Quantumized approach of load scheduling in fog computing environment for IoT applications

Bhatia, Munish; Sood, Sandeep K.; Kaur, Simranpreet

doi:10.1007/s00607-019-00786-5

Cited by 32 publications

(19 citation statements)

References 17 publications

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“…In addition, the proposed resource classification algorithm SoftMax-DNN outperforms others. The SoftMax-DNN obtains the highest sensitivity (92.45), specificity (93.12), precision (92.34), coverage (95.47) and attains the lowest error rate for the measures, such as RASE (2.32), RRSE (2.98), and MAE (2.75) when compared to QCI-NN [20]. Therefore, the proposed system produces more desirable results, especially in terms of the speed of data transmission and energy saving.…”

Section: Discussionmentioning

confidence: 99%

“…Authors have used threshold-based secret sharing that divides into parts to be stored by the IoT devices on the network to retrieve the information by the devices in a collaborative fashion. In another work, Munish Bhatia et al [20] have proposed a quantumized scheme for scheduling tasks in the fog computing environment. A nodespecific metric for measuring the level of computing of the fog system was specifically denoted as the node computing index.…”

Section: Related Workmentioning

confidence: 99%

“…A nodespecific metric for measuring the level of computing of the fog system was specifically denoted as the node computing index. The authors have performed a comparative analysis with existing models and found to be superior [20]. Jafar A. Alzubi et al [21] have devised a scheme to provide security for IIoT data transfer using cloud services with a Hashed Needham Schroeder (HNS) cost-optimized deep machine Learning (CODML) technique that shows the need to deliver IIoT security.…”

Section: Related Workmentioning

confidence: 99%

“…A comparison between the proposed SoftMax-DNN for resource classification and the state-of-the-art forecasting models that include QCI-Neural Network is done in this section [20]. Statistical aspects, including but not limited to the precision, sensitivity, specificity, and the probability of coverage.…”

Section: Analysis Of Softmax-dnn Classifiermentioning

confidence: 99%

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A Secure Industrial Internet of Things (IIoT) Framework for Resource Management in Smart Manufacturing

Abuhasel

Khan

2020

IEEE Access

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The Industrial Internet of Things (IIoT) can transform an existing isolated industrial system to a connected network. The IIoT and the related wireless connectivity requirements for industrial sensors are very significant. The deployed sensors in IIoT monitors the conditions of the industrial devices and machines. Therefore, reliability and security become the most important concerns in IIoT. This introduces many familiar and ever-increasing risks associated with the industrial system. The IIoT devices can be vulnerable to vast array of viruses, threats, and attacks. Therefore, an efficient protection strategy is required to ensure that the millions of IIoT devices are safe from these risks. However, resource constraint IIoT devices have not been designed to have effective security features. Due to this, in recent years, cloud, fog, and edge-based IIoT has received great attention in the research community. The computationally intensive tasks such as security, data analytics, decision making, and reporting are performed at the cloud or fog using a powerful computing infrastructure. The data security of the IIoT device has been provided by employing improved Rivest-Shamir-Adelman (RSA) and hash signatures. The proposed RSA algorithm has a four-prime number of 512-bits. The device authentication is performed by employing a hash signature. For long network life, an efficient clustering technique for the sensor devices which is based on node degree(N), distance from the cluster(D), residual energy(R), and fitness (NDRF) has been proposed. The fitness of the sensor nodes is computed using the Salp swarm algorithm (SSA). In order to reduce the latency and communication overhead for IIoT devices a resource scheduling using SoftMax deep neural network (DNN) is proposed. All the requests coming from the cluster head are classified using SoftMax-DNN for best resource scheduling on the basis of storage, computing, and bandwidth requirements. The proposed framework produces superior results, especially in terms of energy consumption, latency, and strength of security.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Analysis Of Softmax-dnn Classifiermentioning

confidence: 99%

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A Secure Industrial Internet of Things (IIoT) Framework for Resource Management in Smart Manufacturing

Abuhasel

Khan

2020

IEEE Access

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“…Bhatia et al [19] Load scheduling algorithm (QCI) has been proposed that is inspired from quantum computing A quantumized scheduling approach has been proposed fog applications. A nodebased computing index is defined for computational capacity estimation iFogSim Wireless applications were considered that were located at end users…”

Section: Notice Board Case Study Has Been Consideredmentioning

confidence: 99%

A systematic study of load balancing approaches in the fog computing environment

Kaur

Aron

2021

J Supercomput

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Internet of Things has been growing, due to which the number of user requests on fog computing layer has also increased. Fog works in a real-time environment and offers from connected devices need to be processed immediately. With the increase in users requests on fog layer, virtual machines (VMs) at fog layer become overloaded. Load balancing mechanism can distribute load among all the VMs in equal proportion. It has become a necessity in the fog layer to equally, and equitably distribute all the workload among the existing VMs in the segment. Till now, many load balancing techniques have been proposed for fog computing. An empirical study of existing methods in load balancing have been conducted, and taxonomy has been presented in a hierarchical form. Besides, the article contains the year-wise comprehensive review and summary of research articles published in the area of load balancing from 2013 to 2020. Furthermore, article also contains our proposed fog computing architecture to resolve load balancing problem. It also covers current issues and challenges that can be resolved in future research works. The paper concludes by providing future directions.

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Leveraging energy‐efficient load balancing algorithms in fog computing

Singh

Kumar

Sharma

et al. 2020

Concurrency and Computation

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Cloud computing and smart gadgets are the need of smart world these days. This often leads to latency and irregular connectivity issues in many situations. In order to overcome this issue, an emerging technique of fog computing is used for cloud and smart devices. A decentralized computing infrastructure in which all the elements, that is, storage, compute, data and the applications in use, are passed in an efficient and logical place between cloud and the data source, is called Fog computing. The cloud computing and services are generally extended by fog computing, which brings the power and advantages of data creation and data analysis at the network edge. Real-time location based services and applications with mobility support are enabled due to the physical proximity of users and high speed internet connection to the cloud. Fog computing is promoted with leveraging load balancing techniques so as to balance the load which is done in two ways, that is, static load balancing and dynamic load balancing. In this paper, different load balancing algorithms are discussed and their comparative analysis has been carried out. Round Robin load balancing is the simplest and easiest load balancing technique to be implemented in fog computing environments. The major problem of Source IP Hash load balancing algorithm is that each change can redirect to anyone with a different server, and thus, is least preferred in fog networks. The mechanisms to make energy efficient load balancing are also considered as the part of this paper.

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Quantumized approach of load scheduling in fog computing environment for IoT applications

Cited by 32 publications

References 17 publications

A Secure Industrial Internet of Things (IIoT) Framework for Resource Management in Smart Manufacturing

A Secure Industrial Internet of Things (IIoT) Framework for Resource Management in Smart Manufacturing

A systematic study of load balancing approaches in the fog computing environment

Leveraging energy‐efficient load balancing algorithms in fog computing

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